Classifier based non-parametric change point detection
copied from cf-staging / changeforestChange point detection aims to identify structural breaks in the probability distribution of a time series. Existing methods either assume a parametric model for within-segment distributions or are based on ranks or distances and thus fail in scenarios with a reasonably large dimensionality.
changeforest implements a classifier-based algorithm that consistently estimates
change points without any parametric assumptions, even in high-dimensional scenarios.
It uses the out-of-bag probability predictions of a random forest to construct a
classifier log-likelihood ratio that gets optimized using a computationally feasible two-step
method.
See [1] for details.
[1] M. Londschien, P. Bühlmann, and S. Kovács (2023). "Random Forests for Change Point Detection" Journal of Machine Learning Research
